Broad-line and Multi-wave Band Emission from Blazars

We study the correlations of the flux of the broad-line emission ($F_{BLR}$) with the X-ray emission flux, optical emission flux at 5500 \AA and radio emission flux at 5 GHz, respectively, for a large sample of 50 Blazars (39 flat-spectrum radio quasars (FSRQs) and 11 BL Lac objects). Our main results are as follows. There are very strong correlations between $F_{BLR}$ and $F_{X}$ and between $L_{BLR}$ and $L_{X}$ in both states for 39 FSRQs and the slopes of the linear regression equations are almost equal to 1. There are weak correlations between $F_{BLR}$ and $F_{X}$ and between $L_{BLR}$ and $L_{X}$ for 11 BL Lac objects in both states, and the slopes of the linear regression equations are close to 1. There are significant correlations between $F_{BLR}$ and $F_{X}$ and between $L_{BLR}$ and $L_{X}$ for 50 blazars in both states, the slopes of both the linear regression equations are also close to 1. These results support a close link between relativistic jets and accretion on to the central Kerr black hole. On the other hand, we find that BL Lac objects have low accretion efficiency $\eta$, whereas FSRQs have high accretion efficiency $\eta$. The unified model of FSRQs and BL Lac objects is also discussed.Comment: 15 pages, 8 figure

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Simulation of Three-Dimensional Free-Surface Dam-Break Flows over a Cuboid, Cylinder, and Sphere

A three-dimensional (3D) numerical study is undertaken to investigate dam-break flows over 3D structures. A two-phase flow model has been developed within the large-eddy simulation (LES) framework. The governing equations have been discretized using the finite-volume method, with the air-water interface being captured using a volume-of-fluid method while the Cartesian cut-cell method deals with complex geometries. The robustness and versatility of the proposed numerical approach are demonstrated first by applying it to a 3D dam-break flow over a cuboid. Good agreement is obtained between the simulation results and the corresponding experimental data and other numerical solutions. Then, a horizontal cylinder and a sphere are subjected to the same dam-break flow. Snapshots of water surface profiles are presented and discussed, and turbulent vortical structures are identified in the flow. In addition, the internal kinematics, hydrodynamic loading on the structure, and energy dissipation during dam-break flow impact are analyzed and discussed, providing more insight into such flows

A conservative and consistent implicit Cartesian cut-cell method for moving geometries with reduced spurious pressure oscillations

A conservative and consistent three-dimensional Cartesian cut-cell method is presented for reducing the spurious pressure oscillations often observed in moving body simulations in sharp-interface Cartesian grid methods. By analysing the potential sources of the oscillation in the cut-cell framework, an improved moving body algorithm is proposed for the cut-cell method for the temporal discontinuity of the solid volume change. Strict conservation of mass and momentum for both fluid and cut cells is enforced through pressure-velocity coupling to reduce local mass conservation errors. A consistent mass and momentum flux computation is employed in the finite volume method. In contrary to the commonly cut-cell methods, an implicit time integration scheme is employed in the present method, which prevents numerical instability without any additional small cut-cell treatment. The effectiveness of the present cut-cell method for reducing spurious pressure oscillations is demonstrated by simulating various two- and three-dimensional benchmark cases (in-line and transversely oscillating cylinder, oscillating and free-falling sphere), with good agreement with previous experimental measurements and other numerical methods available in the literature

A large-eddy-simulation-based numerical wave tank for three-dimensional wave-structure interaction

A three-dimensional numerical wave tank (NWT) based on the open-source large eddy simulation (LES) code Hydro3D is introduced. The code employs the level set and immersed boundary methods to enable accurate computations of the deformation of the water surface and to account for solid structures in the fluid domain, respectively. The spatially-filtered Navier–Stokes (N–S) equations are solved on a staggered Cartesian grid using the finite difference method while time advancement is achieved using the fractional-step method based with a three-step Runge–Kutta scheme. Velocities and pressure are coupled with the Poisson equation and its solution is obtained via a multi-grid technique. The code is then applied to predict the progression and damping of monochromatic waves and the interaction of non-linear waves with various submerged obstacles. The accuracy of Hydro3D is confirmed by comparing numerical results with data of previously reported laboratory experiments. Comparisons of numerically predicted and measured water-levels, local velocity and pressure fields and forces acting on structures under the influence of incoming waves with laboratory data are convincing and confirm that the code is able to predict accurately three-dimensional wave-structure interaction